检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

848 篇 会议
120 篇 期刊文献

馆藏范围

968 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

621 篇 工学
- 331 篇 信息与通信工程
- 329 篇 计算机科学与技术...
- 254 篇 软件工程
- 112 篇 电子科学与技术（可...
- 89 篇 控制科学与工程
- 59 篇 光学工程
- 59 篇 生物工程
- 55 篇 机械工程
- 53 篇 仪器科学与技术
- 53 篇 航空宇航科学与技...
- 50 篇 电气工程
- 46 篇 化学工程与技术
- 31 篇 生物医学工程（可授...
- 24 篇 建筑学
- 24 篇 土木工程
- 20 篇 动力工程及工程热...
- 18 篇 安全科学与工程
- 16 篇 交通运输工程
- 14 篇 环境科学与工程（可...
323 篇 理学
- 194 篇 数学
- 65 篇 生物学
- 58 篇 物理学
- 50 篇 统计学（可授理学、...
- 47 篇 化学
- 21 篇 系统科学
- 15 篇 大气科学
116 篇 管理学
- 64 篇 管理科学与工程(可...
- 55 篇 图书情报与档案管...
- 20 篇 工商管理
19 篇 医学
- 15 篇 基础医学(可授医学...
18 篇 农学
11 篇 法学
11 篇 军事学
9 篇 教育学
7 篇 经济学
2 篇 艺术学
1 篇 文学

主题

91 篇 synthetic apertu...
52 篇 feature extracti...
30 篇 training
25 篇 accuracy
23 篇 object detection
22 篇 radar
22 篇 radar imaging
20 篇 image segmentati...
20 篇 convolution
17 篇 semantics
17 篇 field programmab...
15 篇 radar polarimetr...
15 篇 computational mo...
15 篇 target recogniti...
15 篇 convolutional ne...
13 篇 target tracking
13 篇 real-time system...
13 篇 remote sensing
11 篇 support vector m...
11 篇 deep learning

机构

215 篇 beijing key labo...
196 篇 hubei province k...
147 篇 beijing key labo...
132 篇 school of comput...
114 篇 college of compu...
72 篇 radar research l...
66 篇 hubei province k...
64 篇 school of inform...
50 篇 beijing institut...
36 篇 advanced technol...
33 篇 beijing key labo...
25 篇 chongqing innova...
21 篇 radar technology...
19 篇 wuhan university...
19 篇 radar research l...
17 篇 hubei key labora...
17 篇 beijing key labo...
17 篇 school of electr...
16 篇 school of comput...
15 篇 hubei province k...

作者

52 篇 ding zegang
40 篇 chen he
39 篇 he chen
35 篇 chen liang
33 篇 hu cheng
30 篇 wang yanhua
28 篇 liang chen
26 篇 xie yizhuang
26 篇 zegang ding
25 篇 li yang
25 篇 zeng tao
24 篇 cheng hu
24 篇 tao zeng
22 篇 xiaolong zhang
22 篇 linbo tang
21 篇 zhang xiaolong
21 篇 tang linbo
20 篇 wang rui
19 篇 teng long
19 篇 liu jun

语言

939 篇 英文
21 篇 其他
8 篇 中文

检索条件"机构=Key Laboratory of Embedded Real-Time Information Processing Technology"

共 968 条记录，以下是331-340 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Implementation of Automatic Face Detection System Based on ARM

Implementation of Automatic Face Detection System Based on A...

引用

2019 IEEE International Conference on Signal, information and Data processing, ICSIDP 2019

作者： Li, Keng Chen, He Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Institute of Technology 5 South Zhongguancun Street Haidian District Beijing China

ISBN: (纸本)9781728123455

The embedded automatic face detection system is to locate the face region in the real scene. In order to reach a real-time performance, We adopt a cascaded structure of the convolution neural network model to solve face detection problem. In hardware implementation, the ARM Cortex-A9 processor is used to achieve the algorithm. And the Neon unit in ARM is used to achieve the parallel operation of the basic data of the algorithm, so the detection algorithm is accelerated to achieve the real-time operation of the automatic face detection system. © 2019 IEEE.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

FPGA-based accelerator for convolution operations

FPGA-based accelerator for convolution operations

引用

2019 IEEE International Conference on Signal, information and Data processing, ICSIDP 2019

作者： Cao, Yunfei Wei, Xin Qiao, Tingting Chen, He Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Institute of Technology 5 South Zhongguancun Street Haidian District Beijing China

ISBN: (纸本)9781728123455

Convolutional neural networks have been widely used in many deep learning applications. Convolutional neural networks have a large number of convolution operations, which poses a huge challenge to real-time performance. In recent years, FPGA implementations of convolutional accelerators have received much attention due to their high performance and energy efficiency. In this paper, we implement an accelerator for convolution operations through the systolic array architecture on Xilinx ZedBoard device. The experimental results show that ours designed accelerators achieving performance density of up to 0.032 Gop/s/DSP. © 2019 IEEE.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Efficient and Lightweight Target Recognition for High Resolution Spaceborne SAR Images

Efficient and Lightweight Target Recognition for High Resolu...

引用

2019 IEEE International Conference on Signal, information and Data processing, ICSIDP 2019

作者： Pan, Yu Tang, Linbo Jing, Donglin Tang, Wei Zhou, Shichao Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9781728123455

Fast and reliable target recognition of the synthetic aperture radar (SAR) images has been widely used in the fields of the marine monitoring, military reconnaissance and strike all over the world. However, due to the difficulty of the intra-class difference and inter-class similarity of the multiclass targets in the high resolution SAR images, the existing methods are difficult to recognize the targets accurately when facing the spaceborne platforms with the high resource constraints. Therefore, in order to solve the above problems, we propose a novel recognition method based on the convolutional neural network (CNN). Firstly, we propose a lightweight CNN framework which regards densely connected convolutional network (DenseNet) as the baseline. Secondly, we advocate a strong discriminative loss function which efficiently improves the recognition accuracy of the targets in the spaceborne SAR images. Experiments are conducted on the TerraSAR dataset and MSTAR dataset to evaluate the proposed method. The results show that our method performs better than the baseline on the both benchmark datasets. © 2019 IEEE.

关键词： Synthetic aperture radar

来源：评论

学校读者我要写书评

暂无评论

Dual-Channel Recalibration and Feature Fusion Method for Liver Image Classification 17th

Dual-Channel Recalibration and Feature Fusion Method for Liv...

引用

17th International Conference on Intelligent Computing, ICIC 2021

作者： Niu, Tingting Zhang, Xiaolong Deng, Chunhua Chen, Ruoqin School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Institute of Big Data Science and Engineering Wuhan University of Science and Technology Wuhan China Hubei Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology Wuhan China Tianyou Hospital Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9783030845285

Aiming at the problem of poor medical image recognition accuracy caused by the large individual differences in liver pathological images and the large single feature size, a liver image classification method based on dual-channel recalibration and feature fusion is proposed. Firstly, we construct a dual-channel recalibration model based on attention mechanism, suppress useless features and calculate different feature channel weights, and embed them into the Inception_ResNet_V2 network structure. Secondly, we design fully connected layers on the basis of two-dimensional and three-dimensional convolutional neural networks, then add feature fusion layers to obtain deep semantic information in different dimensions. Finally, we use pre-training models to initialize the network structure and input the fused features into the XGBoost classifier for classification prediction. Experiments have been carried out with liver cancer race imaging datasets and a hospital patient dataset. Experimental results show that this method is superior to previous ones. © 2021, Springer Nature Switzerland AG.

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

Binary Separable Convolutional: An Efficient Fast Image Classification Method

Binary Separable Convolutional: An Efficient Fast Image Clas...

引用

2019 IEEE International Conference on Signal, information and Data processing, ICSIDP 2019

作者： Jing, Donglin Tang, Linbo Pan, Yu Tang, Wei Zhou, Shichao Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9781728123455

The training and running of neural network require large computational space and memory space, which makes it difficult to deploy on a resource-constrained embedded sys-tems. To address this limitation, we introduce a two-stage pipeline: Depth separable, local binary. Our method is divided into two steps. Firstly, a deep separable convolution is applied to each input channel. Then, point-by-point convolution is applied to the feature map obtained by the filter. In the second step, we use local binarization method to initialize the filter corresponding to the input channel into sparse binary. In network training, the sparse binary filter remains fixed and only needs to train convolution of 11 size. Our experimental results show that, on the basis of approximate accuracy with the original network, we have reduced the number of convolution parameters by 9x to 10x, and reduced the training time and testing time to by 2x. Our compression method helps to deploy complex neural networks on resource-constrained embedded platform. © 2019 IEEE.

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

Hyperspectral inversion for soil moisture and temperature based on Gaussian process regression

Hyperspectral inversion for soil moisture and temperature ba...

引用

2019 IEEE International Conference on Signal, information and Data processing, ICSIDP 2019

作者： Li, Zhen Deng, Chenwei Zhao, Baojun Tian, Yibing Huang, Yun Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9781728123455

The soil moisture and temperature significantly influence the natural environment. Hyperspectral remote sensing can serve as a pivotal technique to monitor soil surface. However, modeling soil parameters encounter the following problems: hyperspectral data is high-dimensional and non-linear, and hyperspectral datasets are of limited size. In this paper, we derive a framework for inversion of soil moisture and temperature. First, wavelet transform is adopted that is able to extract the main structure of spectrum curve and reduce the dimensionality of the hyperspectral data. Then, Gaussian process regression (GPR), which is suitable for small sample data, is applied to predict the soil moisture and temperature. The experimental results show that our model outperforms other methods in estimating soil character. © 2019 IEEE.

关键词： Soil temperature

来源：评论

学校读者我要写书评

暂无评论

Moving target detection based on Circular Video SAR

Moving target detection based on Circular Video SAR

引用

2019 IEEE International Conference on Signal, information and Data processing, ICSIDP 2019

作者： Wang, Hongshuo Zhao, Baojun Yang, Xingsha Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9781728123455

Video Synthetic Aperture Radar (Video-SAR) achieves radar video observation by continuously monitoring a certain area to form continuous image data of the target, and it plays an important role in civil and military fields. The SAR image of the moving target is defocused and deviates from its true position, but in its real position, a shadow area similar to the target size is generated. In this paper, the shadow area is used to detect the moving target. Firstly, the image sequence is de-noised by wavelet analysis. Then the maximum threshold segmentation of Tsallis gray entropy is performed. The SIFT and FAST algorithms are used to detect and match the corners of each frame. At the same time, the RANSAC algorithm is used to optimize the pairing results to complete the image registration. Finally, the moving target is extracted by the three-frame difference method, and the result is marked to the corresponding position of the original image. Based on the Video SAR segment published by Sandia Labs in the United States, the detection of the largest moving vehicle in the video is realized, and the effectiveness and anti-interference of the proposed algorithm is verified. © 2019 IEEE.

关键词： Synthetic aperture radar

来源：评论

学校读者我要写书评

暂无评论

Weighted Anomaly Detection Algorithm Based on Local Density

Weighted Anomaly Detection Algorithm Based on Local Density

引用

2019 IEEE International Conference on Signal, information and Data processing, ICSIDP 2019

作者： Tian, Yibing Tang, Linbo Li, Zhen Huang, Yun Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9781728123455

Anomaly detection for hyperspectral imagery (HSI) has drawn a lot of attention lately. In the past decades, several methods for detecting anomalies in HSI have been developed. The Reed-Xiaoli detector (RXD) is one of the most widely used methods for anomaly detection. However, RXD assumes that background follow the Gaussian distribution, not always conformed to the real case. In order to solve this problem, we improve the RX anomaly detection algorithm by weighting changes of background. First, the concentric double-window model is utilized to obtain the local densities of each background pixels. Then, the local densities are applied to weight background mean and covariance matrix to reduce the influence of abnormal pixels in the background. The experimental results show that our method outperforms other approaches in anomaly detection. © 2019 IEEE.

关键词： Covariance matrix

来源：评论

学校读者我要写书评

暂无评论

Ship Detection in Synthetic Aperture Radar Imagery Based on Discriminative Dictionary Learning 6

Ship Detection in Synthetic Aperture Radar Imagery Based on ...

引用

6th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2019

作者： Wang, Yun Chen, Liang Shi, Hao Zhang, Bocheng Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9781728129129

The ship target detection technology based on SAR image has important significance in military and civil field applications and is one of the research hotspots at this stage. In this paper, the research work on typical problems in SAR image ship target detection is carried out. A ship target detection algorithm based on discriminative dictionary learning is proposed, which mainly includes image denoising, candidate region extraction and candidate region identification. Firstly, an adaptive non-local filtering method is used to denoise the SAR image. Then the gradient feature map reconstruction algorithm is used to extract the candidate regions. Finally, the category constrained discriminative dictionary learning method is used to classify the candidate regions. The algorithm is based on GF-3 and Terra SAR data. The experimental results show that the proposed algorithm has strong robustness and adaptability. © 2019 IEEE.

关键词： Synthetic aperture radar

来源：评论

学校读者我要写书评

暂无评论

A SAR target recognition method with frequency and spatial domain enhancement

A SAR target recognition method with frequency and spatial d...

引用

2019 IEEE International Conference on Signal, information and Data processing, ICSIDP 2019

作者： Wang, Zhiru Chen, Liang Qi, Baogui Wang, Guanqun Shi, Hao Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9781728123455

Synthetic aperture radar (SAR) image target recognition is a hot issue in remote sensing image application. High accuracy SAR target recognition is truly important in both military and civilian fields. Recently, convolutional neural networks (CNNs) have played an important role in the field of SAR image target recognition, however, most of the existing networks incurs some additional problems such as ignoring the influence of speckle noise on the target recognition process, so the recognition accuracy is low. To cope with these problems, this paper proposes a novel SAR image target recognition method based on CNN with frequency and spatial domain enhancement. First, the image is transformed by Gabor feature descriptor in different frequency directions to generate a plurality of feature maps. Then, the image is spatially enhanced using a Laplace transform. The feature maps which obtained by the frequency and spatial domain enhancement together with the original image are used as the network input. Finally, the improved deep residual network (ResNet) is used to complete the target recognition task. Experimental results demonstrate that the proposed method achieves a state-of-the-art accuracy on the MSTAR dataset. © 2019 IEEE.

关键词： Synthetic aperture radar

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共97页 << < 30 31 32 33 34 35 36 37 38 39 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：